Implementation Science for AI Integration in Digital Health Systems

How the Protocol Institute is Adopting AI

What building a research institute at 100% adoption actually looks like

Americans Turning to AI to Supplement Healthcare Visits

One in four Americans use AI for health information. Most do so to supplement care, but some are using AI in place of a provider visit when barriers arise.

Implementation of Digital Monitoring Services During the COVID-19 Pandemic for Patients With Chronic Diseases: Design Science Approach

Background: The COVID-19 pandemic is straining health systems and disrupting the delivery of health care services, in particular, for older adults and people with chronic conditions, who are particularly vulnerable to COVID-19 infection. Objective: The aim of this project was to support primary health care provision with a digital health platform that will allow primary care physicians and nurses to remotely manage the care of patients with chronic diseases or COVID-19 infections. Methods: For the rapid design and implementation of a digital platform to support primary health care services, we followed the Design Science implementation framework: (1) problem identification and motivation, (2) definition of the objectives aligned with goal-oriented care, (3) artefact design and development based on Scrum, (4) solution demonstration, (5) evaluation, and (6) communication. Results: The digital platform was developed for the specific objectives of the project and successfully piloted in 3 primary health care centers in the Lisbon Health Region. Health professionals (n=53) were able to remotely manage their first patients safely and thoroughly, with high degrees of satisfaction. Conclusions: Although still in the first steps of implementation, its positive uptake, by both health care providers and patients, is a promising result. There were several limitations including the low number of participating health care units. Further research is planned to deploy the platform to many more primary health care centers and evaluate the impact on patient’s health related outcomes.

As more Americans adopt AI tools, fewer say they can trust the results | TechCrunch

AI adoption is rising in the U.S., but trust remains low, with most Americans concerned about transparency, regulation, and the technology’s broader societal impact, according to a new Quinnipiac poll.

KFF Tracking Poll on Health Information and Trust: Use of AI For Health Information and Advice | KFF

This poll finds that about as many adults are turning to AI for health information as social media, with health care costs and access driving many users, particularly younger users.

Half of Americans report using AI services, with information and productivity leading use cases

New Epoch AI/Ipsos poll reveals high AI engagement, diverse use cases, and emerging workplace integration

Measuring the Impact of Early-2025 AI on Experienced Open-Source...

Despite widespread adoption, the impact of AI tools on software development in the wild remains understudied. We conduct a randomized controlled trial (RCT) to understand how AI tools at the...

We Need to Know More About How AI is Affecting Mental Health

The public, mental health practitioners, and policymakers don’t know enough about the impacts of AI use on the human psyche, writes Chris Mills Rodrigo.

Charting AI’s Role in Scientific Discovery — Renaissance Philanthropy – A brighter future for all through science, technology, and innovation

Renaissance Philanthropy, with support from Google.org , is conducting a landscape study of AI integration in scientific research — and we want your perspective.

What Can We Learn From the FDA Model for AI Regulation? - AI Now Institute

After holding a rapid deliberation late last year that convened deep experts on the FDA alongside key participants in the AI policy debate, we pulled together several immediate insights into a memo. Though it was clear the path forward isn’t to port over any single regulatory model wholesale, hosting a deep dive into the Food […]

How AI For Women’s Health Is Being Built Differently

Healthcare AI mirrors decades of gender bias. Women-led startups are rebuilding it to reduce misdiagnosis, dismissal, and fragmented care.

How public involvement can improve the science of AI

As AI systems from decision-making algorithms to generative AI are deployed more widely, computer scientists and social scientists alike are being called on to provide trustworthy quantitative evaluations of AI safety and reliability. These calls have included demands from affected parties to be given a seat at the table of AI evaluation. What, if anything, can public involvement add to the science of AI? In this perspective, we summarize the sociotechnical challenge of evaluating AI systems, which often adapt to multiple layers of social context that shape their outcomes. We then offer guidance for improving the science of AI by engaging lived-experience experts in the design, data collection, and interpretation of scientific evaluations. This article reviews common models of public engagement in AI research alongside common concerns about participatory methods, including questions about generalizable knowledge, subjectivity, reliability, and practical logistics. To address these questions, we summarize the literature on participatory science, discuss case studies from AI in healthcare, and share our own experience evaluating AI in areas from policing systems to social media algorithms. Overall, we describe five parts of any quantitative evaluation where public participation can improve the science of AI: equipoise, explanation, measurement, inference, and interpretation. We conclude with reflections on the role that participatory science can play in trustworthy AI by supporting trustworthy science.

Implementing an online pharmaceutical service using design science research

The rising prevalence of chronic diseases is pressing health systems to introduce reforms. Primary healthcare and multidisciplinary models have been suggested as approaches to deal with this challenge, with new roles for nurses and pharmacists being advocated. More recently, implementing healthcare based on information systems and technologies (e.g. eHealth) has been proposed as a way to improve health services. However, implementing online pharmaceutical services, including their adoption by pharmacists and patients, is still an open research question. In this paper we present ePharmacare, a new online pharmaceutical service implemented using Design Science Research.

AI linked to explosion of low-quality biomedical research papers

Analysis flags hundreds of studies that seem to follow a template, reporting correlations between complex health conditions and single variables based on publicly available data sets.

Responsible AI Principles and Approach | Microsoft AI

Discover Microsoft AI tools, industry-specific governance solutions, and responsible AI practices to make smarter, more informed decisions about AI implementation.